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AlexeyAB / darknet forked from pjreddie/darknet Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up Dismiss Windows and Linux version of Darknet Yolo v3 & v2 Neural Networks for object detection (Tensor Cores are used) http://pjreddie.com/darknet/ # dnn # deep-learning # deep-neural-networks # deep-learning-tutorial # object-detection # computer-vision # neural-network 1,390 commits 1 branch 4 releases 39 contributors View license Branch: master New pull request This branch is 1063 commits ahead, 109 commits behind pjreddie:master. AlexeyAB Update Readme.md .circleci 3rdparty build/darknet cfg Moved all OpenCV functions to the image_opencv.cpp/h x64 only Added [sam] layer Fixed yolo_cpp_dll.vcxproj Find File Clone or download Pull request Compare Latest commit 4c315ea 13 days ago 5 months ago 5 months ago 19 days ago 2 months ago

cmake/Modules upgrade FindCUDNN module to better deal with dlls and targets data include scripts src .gitignore .travis.yml Added yolov3-openimages.cfg and SELU activation Added assisted_excitation_forward for training on CPU Minor output fixes Compile fix restore shared lib as default fixes for modern clang on linux CMakeLists.txt restrict the opencv module selection to the only problematic configur… DarknetConfig.cmake.in enable darknet as a library also when only MSVC build tools are insta… LICENSE Makefile README.md appveyor.yml build.ps1 build.sh darknet.py darknet_video.py image_yolov2.sh image_yolov3.sh Update LICENSE Compile fix Update Readme.md option to build project using cpp compiler if c not working/available option to build project using cpp compiler if c not working/available option to build project using cpp compiler if c not working/available Added ability to use letter_box resizing in darknet.py sample Another minor fix move Stb lib to 3rdparty folder move Stb lib to 3rdparty folder json_mjpeg_streams.sh move Stb lib to 3rdparty folder net_cam_v3.sh move Stb lib to 3rdparty folder 21 days ago 10 months ago 14 days ago 2 months ago 14 days ago 21 days ago 22 days ago 20 days ago 23 days ago 3 years ago 19 days ago 13 days ago 28 days ago 28 days ago 28 days ago 2 months ago 7 months ago 5 months ago 5 months ago 5 months ago 5 months ago

video_v2.sh video_yolov3.sh README.md move Stb lib to 3rdparty folder move Stb lib to 3rdparty folder 5 months ago 5 months ago Yolo-v3 and Yolo-v2 for Windows and Linux (neural network for object detection) - Tensor Cores can be used on Linux and Windows More details: http://pjreddie.com/darknet/yolo/ buildbuild passing passing buildbuild passing passing contributors contributors 3939 license license Unlicense Unlicense Requirements (and how to install dependecies) Pre-trained models Explanations in issues Yolo v3 in other frameworks (TensorRT, TensorFlow, PyTorch, OpenVINO, OpenCV-dnn,...) Datasets 0. Improvements in this repository 1. How to use 2. How to compile on Linux 3. How to compile on Windows Using vcpkg Legacy way 4. How to train (Pascal VOC Data)

5. How to train with multi-GPU: 6. How to train (to detect your custom objects) 7. How to train tiny-yolo (to detect your custom objects) 8. When should I stop training 9. How to calculate mAP on PascalVOC 2007 10. How to improve object detection 11. How to mark bounded boxes of objects and create annotation files 12. How to use Yolo as DLL and SO libraries

mAP@0.5 (AP50) https://pjreddie.com/media/files/papers/YOLOv3.pdf YOLOv3-spp better than YOLOv3 - mAP = 60.6%, FPS = 20: https://pjreddie.com/darknet/yolo/ Yolo v3 source chart for the RetinaNet on MS COCO got from Table 1 (e): https://arxiv.org/pdf/1708.02002.pdf

Yolo v2 on Pascal VOC 2007: https://hsto.org/files/a24/21e/068/a2421e0689fb43f08584de9d44c2215f.jpg Yolo v2 on Pascal VOC 2012 (comp4): https://hsto.org/files/3a6/fdf/b53/3a6fdfb533f34cee9b52bdd9bb0b19d9.jpg Requirements Windows or Linux CMake >= 3.8 for modern CUDA support: https://cmake.org/download/ CUDA 10.0: https://developer.nvidia.com/cuda-toolkit-archive (on Linux do Post-installation Actions) OpenCV >= 2.4: use your preferred package manager (brew, apt), build from source using vcpkg or download from OpenCV official site (on Windows set system variable OpenCV_DIR = C:\opencv\build - where are the include and x64 folders image) cuDNN >= 7.0 for CUDA 10.0 https://developer.nvidia.com/rdp/cudnn-archive (on Linux copy cudnn.h , libcudnn.so ... as desribed here https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installlinux-tar , on Windows copy cudnn.h , cudnn64_7.dll , cudnn64_7.lib as desribed here https://docs.nvidia.com/deeplearning/sdk/cudnn- install/index.html#installwindows ) GPU with CC >= 3.0: https://en.wikipedia.org/wiki/CUDA#GPUs_supported on Linux GCC or Clang, on Windows MSVC 2015/2017/2019 https://visualstudio.microsoft.com/thank-you- downloading-visual-studio/?sku=Community Compiling on Windows by using Cmake-GUI as on this IMAGE: Configure -> Optional platform for generator (Set: x64) -> Finish -> Generate -> Open Project -> x64 & Release -> Build -> Build solution Compiling on Linux by using command make (or alternative way by using command: cmake . && make ) Pre-trained models There are weights-file for different cfg-files (smaller size -> faster speed & lower accuracy: yolov3-openimages.cfg (247 MB COCO Yolo v3) - requires 4 GB GPU-RAM: https://pjreddie.com/media/files/yolov3- openimages.weights

yolov3-spp.cfg (240 MB COCO Yolo v3) - requires 4 GB GPU-RAM: https://pjreddie.com/media/files/yolov3- spp.weights yolov3.cfg (236 MB COCO Yolo v3) - requires 4 GB GPU-RAM: https://pjreddie.com/media/files/yolov3.weights yolov3-tiny.cfg (34 MB COCO Yolo v3 tiny) - requires 1 GB GPU-RAM: https://pjreddie.com/media/files/yolov3- tiny.weights yolov2.cfg (194 MB COCO Yolo v2) - requires 4 GB GPU-RAM: https://pjreddie.com/media/files/yolov2.weights yolo-voc.cfg (194 MB VOC Yolo v2) - requires 4 GB GPU-RAM: http://pjreddie.com/media/files/yolo-voc.weights yolov2-tiny.cfg (43 MB COCO Yolo v2) - requires 1 GB GPU-RAM: https://pjreddie.com/media/files/yolov2- tiny.weights yolov2-tiny-voc.cfg (60 MB VOC Yolo v2) - requires 1 GB GPU-RAM: http://pjreddie.com/media/files/yolov2-tiny- voc.weights yolo9000.cfg (186 MB Yolo9000-model) - requires 4 GB GPU-RAM: http://pjreddie.com/media/files/yolo9000.weights Put it near compiled: darknet.exe You can get cfg-files by path: darknet/cfg/ Yolo v3 in other frameworks TensorFlow: convert yolov3.weights / cfg files to yolov3.ckpt / pb/meta : by using mystic123 or jinyu121 projects, and TensorFlow-lite Intel OpenVINO 2019 R1: (Myriad X / USB Neural Compute Stick / Arria FPGA): read this manual OpenCV-dnn is a very fast DNN implementation on CPU (x86/ARM-Android), use yolov3.weights / cfg with: C++ example, Python example PyTorch > ONNX > CoreML > iOS how to convert cfg/weights-files to pt-file: ultralytics/yolov3 and iOS App TensorRT for YOLOv3 (-70% faster inference): Yolo is natively supported in DeepStream 4.0 TVM - compilation of deep learning models (Keras, MXNet, PyTorch, Tensorflow, CoreML, DarkNet) into minimum deployable modules on diverse hardware backends (CPUs, GPUs, FPGA, and specialized accelerators): https://tvm.ai/about

Datasets MS COCO: use ./scripts/get_coco_dataset.sh to get labeled MS COCO detection dataset OpenImages: use python ./scripts/get_openimages_dataset.py for labeling train detection dataset Pascal VOC: use python ./scripts/voc_label.py for labeling Train/Test/Val detection datasets ILSVRC2012 (ImageNet classification): use ./scripts/get_imagenet_train.sh (also imagenet_label.sh for labeling valid set) German/Belgium/Russian/LISA/MASTIF Traffic Sign Datasets for Detection - use this parsers: https://github.com/angeligareta/Datasets2Darknet#detection-task List of other datasets: https://github.com/AlexeyAB/darknet/tree/master/scripts#datasets Examples of results Others: https://www.youtube.com/user/pjreddie/videos

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GitHub - AlexeyAB_darknet_ Windows and Linux version of Darknet ....pdf

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